1. Technical Field
The present disclosure relates to image detection and, more specifically, to circular intensity distribution analysis for the detection of convex, concave and flat surfaces.
2. Discussion of the Related Art
Computer vision is the technical field of using computers to interpret visual data such as two and three-dimensional images. Computer vision techniques may be instrumental in interpreting medical images, for example, to perform computer assisted diagnosis (CAD).
Traditional approaches to interpreting medical images, such as magnetic resonance images (MRI) and computer tomography (CT) images involve the acquisition of image data using a medical image device, for example, an MRI or a CT scanner. The acquired medical image data may then be rendered into a three-dimensional volume. A trained medical practitioner, for example, a radiologist, may then analyze the volume image, for example, over a series of consecutive two-dimensional volume slices, to detect the presence of disease or injury.
In the healthcare industry, however, there is increasing pressure to reduce the expense of medical image analysis while increasing efficacy. Accordingly, the medical practitioner must be able more accurately diagnose disease and injury in only a small amount of time.
By using CAD techniques to analyze medical image, disease and injury may be more accurately diagnosed in less time than when using traditional manual approaches. When using CAD techniques, one or more regions of interest may be automatically highlighted, or otherwise identified, for the benefit of the medical practitioner who ultimately renders a diagnosis.
In contributing to such a diagnosis, it is often useful to characterize the shape of an object. By characterizing the object's shape, important insights into the nature of the shape may be obtained. For example, it is particularly beneficial to characterize a shape of a potentially curved object as either concave, convex or flat.
One approach to characterizing the shape of a surface of the object is to match the surface in question against one or more geometric primitives. In this way, parametric descriptions of the surfaces may be achieved. The geometric primitives may each be compared to the surface in question and the residual of the fitting may be analyzed. For example, a curve fitting approach may be taken to find a curvature that best fits the surface in question. Other approaches may use curvature to identify the loci where the mean or Gaussian curvature indicates a peak, and the surface in question may be mapped to the Gaussian sphere.
When analyzing synthetic structures, geometric contours may be fit to one or more primitives with relative ease. This is because synthetic structures may have prominent features and strong geometric definition. However, when analyzing anatomical structures, such approximations may be substantially more difficult and surface noise may become a large factor in segmentation.